Moist physics are conventionally handled by a suite of human-devised parametrizations encorporating physical constraints and empirical knowledge.
These typically assume an analytical sub-grid-scale cloud model in statistical equilibrium with the large-scale environment [1]}, but
recent work proposes parameterizing moist physics using machine learning models trained from either higher-fidelity simulations [2]}, [3]}, [4]}, [5]}, [6]} or reanalysis data [7]}.
